UltraSoundNeRF: Sonographic neural reflection field for novel view synthesis

Current state-of-the-art novel view synthesis methods generate natural scene images indistinguishable from real images. However, methods developed for ultrasound imaging often struggle with semantic accuracy, physical plausibility, or large domain gaps from real ultrasound images. In Ultra-NeRF, we address these limitations by reconstructing a neural field of acoustic properties and enabling novel view synthesis of ultrasound images through an ultrasound-specific forward synthesis model. While Ultra-NeRF successfully captures key ultrasound characteristics resulting from sound-wave-based imaging, it lacks interpretability in the acoustic parameter space, limiting practical utility and in-depth analysis of the acoustic properties. In this work, we build upon our previous conference paper by shifting the emphasis from generating visually plausible images with Ultra-NeRF to ensuring the physical accuracy of the underlying neural field. To this end, we revisit neural fields for ultrasound and introduce Sonographic Neural Reflection Field which we call UltraSoundNeRF (or USNeRF in short form) by redesigning Ultra-NeRF's differentiable forward synthesis model and incorporating physics-inspired regularization that results from properties of ultrasound imaging. We extend the Ultra-NeRF dataset by introducing experiments on patient lower leg data and two new scenarios: an ex-vivo phantom and a calibration phantom. The ex-vivo phantom demonstrates that the proposed method can reconstruct real biological tissue, while the calibration phantom shows that incorporating regularization yields attenuation estimates that more closely reflect the expected physical values, and experiments on patient lower leg data. While reconstruction accuracy remains comparable to the original method, our approach significantly enhances the interpretability of acoustic properties across materials with diverse characteristics.